Download does metabolic theory apply to community ecology? it`s a matter of

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METABOLIC THEORY OF ECOLOGY
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Ecology, 85(7), 2004, pp. 1797–1799
q 2004 by the Ecological Society of America
DOES METABOLIC THEORY APPLY TO COMMUNITY ECOLOGY?
IT’S A MATTER OF SCALE
DAVID TILMAN,1 JANNEKE HILLERISLAMBERS, STAN HARPOLE, RAY DYBZINSKI, JOE FARGIONE,
CHRIS CLARK, AND CLARENCE LEHMAN
Department of Ecology, Evolution and Behavior, 1987 Upper Buford Circle, University of Minnesota, St. Paul,
Minnesota 55108 USA
Manuscript received 3 November 2003. Corresponding
Editor: A. A. Agrawal. For reprints of this Forum (including
the MacArthur Award paper), see footnote 1, p. 1790.
1 E-mail: [email protected]
Might growth rate (McMahon and Bonner 1983) be the
controlling variable, rather than metabolic rate? Or
might body size and metabolism be easily measured
surrogates of the actual traits that determine species
interactions and abundances? After all, within the
framework of community ecology, it is traits such as
competitive ability, dispersal, and predator defenses,
and not metabolism and body size, that directly determine which species win or lose, which persist and speciate, or which go extinct.
Of the various predictions that Brown et al. (2004)
derived, perhaps the most surprising to community
ecologists may be that within a trophic level, species
of vastly different body sizes should get equal shares
of their limiting resources. Simply put, all of the herbivorous arthropods within a 10-fold range of body
sizes should consume roughly the same amount of food
as all of the herbivorous mammals within a 10-fold
range of body sizes. This suggests that, on average,
species should be getting approximately equal-sized
‘‘slices’’ of the limiting resources for which they compete. Does this mean that there are limits to similarity
that lead to relatively even packing of competing species along gradients? If so, what mechanisms could
cause this, and how would community ecologists test
this prediction in the field?
Another prediction made by Brown et al. (2004) is
that higher temperatures in the tropics may lead to faster metabolism, shorter generation times, and thus faster
rates of speciation, accounting for the latitudinal biodiversity gradient. This is an interesting alternative to
other hypotheses for latitudinal diversity gradients,
such as the hypothesis that diversity is lower toward
the poles because of higher rates of extinction from a
less stable climate and glaciation, or that there are fewer ways to survive and grow in progressively colder
habitats because life is a water-based (not an ice-based)
process. The metabolic approach may also offer insight
into r vs. K selection. Brown et al. (2004) suggest that
species selected for fast population growth rates would
necessarily have higher metabolism, smaller body size,
and higher temperature. K-selected species are not necessarily selected for slow population growth rates, but
this may be a consequence of selection for predation
resistance or resource use efficiency, which often are
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When the seven of us read and discussed Brown et
al. (2004), there were moments of insight, of enthusiastic consensus, and of strongly divergent opinion.
We agreed that the empirical relations and scaling theory of Brown et al. (2004) hold great appeal because
of their power to abstract and simplify some of the
complexity of nature. The earth harbors several million
species, each having unique aspects to its morphology,
physiology, and life history. A fundamental goal of
science is to simplify and explain such complexity.
Brown et al. (2004) do just this. They have documented
robust patterns relating the body size and temperature
of species to their basal metabolic rate; plotted on log–
log scales, these empirical functions are well fit by
straight lines. Moreover, they have used these scaling
relations to make numerous predictions about other patterns and processes, thus greatly extending an approach
that already had been shown to have considerable power (e.g., Huxley 1932, McMahon and Bonner 1983,
Peters 1983, May 1986).
One question that generated considerable debate
among us was whether metabolic scaling theory represents a fundamental mechanism that has shaped life
on earth, or whether it is a description of correlated
patterns of as yet poorly known causes. Brown et al.
(2004) hypothesize that scaling relations have a fundamental basis that comes from the universality of metabolic activation energy and of the fractal branching
networks that determine resource distribution within
individual organisms. This elegant hypothesis intrigued
us. It brought to the forefront questions raised when
we spent a semester last year reading many of the papers upon which Brown et al. (2004) is based. Are
slopes really multiples of ¼, or is this just the best
small-whole-number ratio approximation? How might
mechanical constraints, which may scale differently
with body size (e.g., McMahon and Bonner 1983), contribute to these patterns? Larger organisms must, after
all, have a higher proportion of their mass in woody
stems or bones or other support tissues that have low
metabolic costs but high costs for their construction.
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METABOLIC THEORY OF ECOLOGY
Ecology, Vol. 85, No. 7
FIG. 1. (A) Predictive power of body size. We used the data in Brown et al. (2004) and simulations to explore the
relationship between the range of organism sizes studied and the explanatory power of size. First, we calculated the unexplained
variance around the regression lines in Figs. 2, 3, 5, and 6 of Brown et al. (2004) using the slope, number of sample points,
and R2 of relationships between size and organismal biomass growth rate, fish mortality, population growth rate, and population
density. Next we calculated R2 for simulated data sets of species covering narrower ranges in body size. To do this, we
randomly sampled 100 ‘‘species’’ from a uniform distribution of body sizes between a specified size range. We next generated
response variables for each species (i.e., predicted biomass growth rate, fish mortality, population growth rate, and population
density) using observed slopes and intercepts and estimated variance. Finally, we used simple linear regressions to calculate
the R2 between organism size and the process of interest. We repeated this 200 times for each of 200 equally distributed size
ranges (from 0 to the size range used in Brown et al. [2004]). Lines connect the mean R2 values for each size range. (B)
Size range of studied organisms, based on papers in the journal Ecology. We examined all recent papers in Ecology that
referenced specific organisms or types of organisms (190 papers from issues 1–9, volume 84, 2003). We tabulated the
organisms described in each paper, assigned them to size classes, and then determined the orders of magnitude size range
(difference between the log10 estimated mass in grams of the largest and smallest organisms). The horizontal axis is organism
size range; the vertical axis is the number of papers that studied species in that range.
enhanced by larger body size. These examples again
suggest that the robustness of scaling relations may
come not from their direct mechanistic relevance, but
from the ability of a single variable, body size, to abstract a suite of correlated traits when making comparisons across broad scales. Whatever the underlying
mechanisms might be, the predictions in Brown et al.
(2004) demonstrate that scaling relations can be a powerful way to reduce dimensionality and to abstract some
of the complexity of nature.
We had considerable debate, though, about the
breadth of applicability of metabolic scaling theory. It
is clear that scaling relationships hold best when examining patterns across a wide spectrum of body sizes
(such as from microbes to megafauna) within an ecosystem, continent, or the globe (Fig. 1A). Because detailed, mechanistic treatments of the interactions
among all species in an ecosystem are impossible, abstraction is essential. Metabolic theory may, for instance, allow better parameterization and understanding of ecosystem nutrient and energy fluxes caused by
the large size range of species, such as from bacteria
to sequoias, in ecosystems.
It is less clear, however, if metabolic scaling will
prove useful in addressing many of the central questions of population and community ecology, such as
population regulation and controls of coexistence, of
species relative abundance patterns, and of diversity
(e.g., Tilman 1999, Hubbell 2001, Sterner and Elser
2002, Chase and Leibold 2003). Much of community
ecology pursues these questions by exploring the mechanisms of local interactions among often similar-sized
species. An analysis of the relations reported by Brown
et al. (2004) shows that the strength of the correlation
between various ecological processes and body size
diminishes as the range in body sizes decreases (Fig.
1A). The variation in species traits that seems so small
when comparing bacteria to elephants looms large
when comparing beech trees to oaks, or a prairie grass
with a prairie forb.
The data presented in Brown et al. (2004) show that
organisms of similar body size can have .20-fold differences in their traits. Moreover, these data show that
body size explains only 2–20% of the observed variance in predicted responses when species fall within a
10-fold range in body sizes (Fig. 1A). Scaling relations
thus have increasingly limited predictive ability in
comparisons of organisms of more similar size. Such
comparisons, however, are a central part of ecology;
roughly half of a sample of papers recently published
in Ecology focused on only one species or on several
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METABOLIC THEORY OF ECOLOGY
at small scales. The vast diversity of alternative roles
that can be filled by organisms of equal body size probably accounts for the $20-fold variation observed
around the mean scaling trend. Perhaps when comparisons are made across larger body size ranges, the constraints of body size and its correlates increasingly predominate over the interspecific trade-offs in resource
use, dispersal, and disease resistance that are the more
proximate determinants of species interactions and
abundance. If, as seems likely, scaling relations do have
their basis in metabolic activation energy, fractal
branching, and structural constraints, then these forces
must be acting at a deeper level, such as by defining
body size and metabolic constraints that shaped the
form and functioning of life as single-celled organisms
evolved into multicellular plants and animals.
In summary, Brown et al. (2004) have provided a
new window through which we can ponder nature. The
simplicity and potential generality of the view that they
provide is welcome; ecology as a discipline cannot afford to wallow in special cases. Metabolic theory provides a unique and insightful macroscopic perspective,
one that appears to have great utility for comparisons
of organisms of vastly different sizes. The possible
causes of these patterns, the applicability of the approach to studies of similar-sized organisms, and the
potential synthesis of mechanistic and macro-ecological approaches are challenges that are likely to be pursued for years to come.
LITERATURE CITED
Brown, J. H., J. F. Gillooly, A. P. Allen, V. M. Savage, and
G. B. West. 2004. Toward a metabolic theory of ecology.
Ecology 85:1771–1789.
Chase, J. M., and M. A. Leibold. 2003. Ecological niches:
linking classical and contemporary approaches. University
of Chicago Press, Chicago, Illinois, USA.
Hubbell, S. P. 2001. The unified neutral theory of biodiversity
and biogeography. Monographs in Population Biology,
Princeton University Press, Princeton, New Jersey, USA.
Huxley, J. S. 1932. Problems in relative growth. Methuen,
London, UK.
May, R. M. 1986. The search for patterns in the balance of
nature: advances and retreats. Ecology 67:1115–1126.
McMahon, T. A., and J. T. Bonner. 1983. On size and
life.Scientific American/W. H. Freeman, New York, New
York, USA.
Peters, R. H. 1983. The ecological implications of body size.
Cambridge University Press, New York, New York, USA.
Sterner, R. W., and J. J. Elser. 2002. Ecological stoichiometry.
Princeton University Press, Princeton, New Jersey, USA.
Tilman, D. 1999. The ecological consequences of changes
in biodiversity: a search for general principles. Ecology 80:
1455–1474.
Tilman, D., P. B. Reich, J. Knops, D. Wedin, T. Mielke,
and C. Lehman. 2001. Diversity and productivity in a
long-term grassland experiment. Science 294:843–845.
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species that were within an order of magnitude in body
mass (Fig. 1B).
Much of our recent work has focused on how the
identity and number of grassland plant species interacting in a local neighborhood influences processes
such as primary productivity. The species that we study
are herbaceous perennials that differ by less than ¾ of
an order of magnitude in adult body size. In our biodiversity experiment (Tilman et al. 2001), the number
of plant species explained 37% of the variance in total
biomass in 2002 (linear regression: N 5 168, P ,
0.0001). Species number and functional group composition explained 68% of this variance in total biomass (multiple regression: F28, 139 5 10.4, P , 0.0001).
The scaling approach, which works so well across large
scales of body size, predicts at most 12% of the variance in various ecological processes for the range of
body sizes in our study (Fig. 1A). Thus, on our scale,
plant functional traits and plant diversity are much
more important than body size. Conversely, our work
on the local neighborhood effects of diversity gives
little, if any, insight into the potential relationship between diversity and productivity on geographic scales
where species come from different species pools and
where other factors, such as climate, soil, and plant
traits, are correlated and change simultaneously.
An analogy and an insight into the power and limits
of the scaling approach come from a consideration of
another complex system with which we are familiar:
computers and related digital devices. Like an organism, a silicon circuit has a metabolism, measured by
how much electricity it consumes. The volume of information that a digital device can process, the airflow
needed to cool it, its reliability and longevity, and other
properties are all a function of its size and, thus, its
metabolism. Such macroscopic properties of digital devices are essential for whole-system tasks like designing power supplies and writing warranties. The relationship of metabolic scaling to ecology is analogous;
it gives significant insights into macroscopic ecological
patterns and predicts other patterns and processes
across large scales and whole systems. Many different
functions, however, can be performed by digital devices with identical sizes and energy demands, just as
many different ecological roles can be performed by
organisms of similar size and temperature. It is these
ecological roles, not metabolisms per se, that determine
species coexistence and abundances and ecosystem
functioning.
One of the mysteries of scaling theory is why it has
such great explanatory power at large scales, but not
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